The Brazilian national prospective active surveillance (AS) cohort of patients with low-risk prostate cancer in the public health system: vigiaSUS study protocol.

Background: Prostate cancer exhibits a very diverse behaviour, with some patients dying from the disease and others never needing treatment. Active surveillance (AS) consists of periodic PSA assessment (prostate-specific antigen), DRE (digital rectal examination) and periodic prostate biopsies. According to the main guidelines, AS is the preferred strategy for low-risk patients, to avoid or delay definitive treatment.

The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study.

Background: The Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating possible negative clinical impacts. Artificial intelligence methods can be an important support for the pathologist, improving Gleason grade classifications.

Detecting and grading prostate cancer in radical prostatectomy specimens through deep learning techniques.

Objectives: This study aims to evaluate the ability of deep learning algorithms to detect and grade prostate cancer (PCa) in radical prostatectomy specimens.

Methods: We selected 12 whole-slide images of radical prostatectomy specimens. These images were divided into patches, and then, analyzed and annotated.